Objective: Among children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data sources.
Methods: We included children and adolescents 0-17 years transported by ambulance to acute care hospitals in 47 states from January 1, 2014 through December 31, 2022. We used 96 predictors, including basic demographic information and neighborhood measures matched to home ZIP code from 5 data sources: EMS records, American Community Survey, Child Opportunity Index, County Health Rankings, and Social Vulnerability Index.
Importance: High emergency department (ED) pediatric readiness is associated with improved survival among children receiving emergency care, but state and national costs to reach high ED readiness and the resulting number of lives that may be saved are unknown.
Objective: To estimate the state and national annual costs of raising all EDs to high pediatric readiness and the resulting number of pediatric lives that may be saved each year.
Design, Setting, And Participants: This cohort study used data from EDs in 50 US states and the District of Columbia from 2012 through 2022.
The quality of emergency department (ED) care for children in the US is highly variable. The National Pediatric Readiness Project aims to improve survival for children receiving emergency services. We conducted a cost-effectiveness analysis of increasing ED pediatric readiness, using a decision-analytic simulation model.
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